Building Powerful Search-Driven Apps with Database-Native Full-Text Search

Modern applications live and die by how easily users can find information. Whether it’s products in an e-commerce store, documents in a knowledge base, or tickets in an internal dashboard, search is often the most critical feature.

Yet many teams over-engineer search from day one—introducing external services, syncing pipelines, and unnecessary infrastructure—when the database itself already provides a powerful solution.

This article explores a lightweight but highly effective methodology: building apps around database-native full-text search, using Supabase and its built-in integration with PostgreSQL full-text search capabilities.

The Core Idea: Search Where the Data Lives

Instead of exporting data into a separate search engine, this approach keeps search inside the primary database.

With Supabase, you can attach a textSearch filter directly to a query. You specify:

  • The column to search

  • The user’s search query

  • Optional parameters that control how the text is parsed and matched

Behind the scenes, PostgreSQL converts plain text into a searchable structure and executes the query efficiently using native full-text search functions. No duplication, no background sync jobs, no extra services.

Why This Works So Well

1. Google-Like Query Syntax

By using the websearch mode, users can search in a way that already feels familiar:

  • Multiple words → logical AND

  • OR between words → logical OR

  • Quoted phrases → exact matches

  • Hyphens → exclusions

Examples:

  • navy shirt

  • "navy shirt"

  • cotton shirt -red

This alone unlocks far more expressive search than basic LIKE queries.

2. Language-Aware Normalization

PostgreSQL applies a language configuration (English by default) that:

  • Normalizes words

  • Handles stemming

  • Improves relevance

You can swap configurations if your app supports multiple languages, without rewriting your search logic.

3. One Database, One Source of Truth

Because search runs directly on your tables:

  • Results are always fresh

  • No risk of stale indexes

  • Access control stays centralized

  • Debugging stays simple

This dramatically reduces operational complexity, especially for small teams and early-stage products.

What Kinds of Apps Can Be Built This Way?

This methodology generalizes far beyond e-commerce.

🛒 Commerce & Marketplaces

  • Product catalogs

  • Second-hand marketplaces

  • Digital goods libraries

📚 Knowledge & Content Platforms

  • Internal documentation

  • Wikis

  • Research archives

  • Policy and legal document search

🧑‍💼 SaaS & Internal Tools

  • CRM systems

  • Support ticket platforms

  • Incident logs

  • HR records

👥 Community & Social Apps

  • Forums

  • Job boards

  • Event listings

  • Creator content platforms

Anywhere users primarily search by keywords, phrases, and exclusions, database-native search shines.

Step-by-step GUIDE

These instructions walk alongside the video and give you exact prompts, terminal commands, and copy/paste snippets to implement full-text search on a Supabase table—using the textSearch() filter and the websearch query style demonstrated in the transcript.

0) Prerequisites

What you need installed

  • Node.js (recommended: latest LTS)

  • npm (bundled with Node)

What you need online

  • A Supabase account + a new project

1) Create a Supabase project + get credentials (Supabase Dashboard)

  1. Go to your Supabase dashboard → New project

  2. Once created, open:

    • Project Settings → API

  3. Copy:

    • Project URL (e.g. https://xxxx.supabase.co)

    • anon public key

✅ These will be used in your local .env.

2) Create a simple “products” table (Supabase SQL Editor)

Prompt (what you’re building)

“Create a products table with a name column that we can full-text search.”

In Supabase:

  1. Go to SQL Editor

  2. Run this SQL:

create table if not exists public.products (
  id bigint generated by default as identity primary key,
  name text not null,
  description text,
  created_at timestamptz default now()
);

Add some sample products

Run:

insert into public.products (name, description) values
  ('Navy Shirt', 'Soft cotton navy tee'),
  ('Red Cotton Shirt', 'Bright red cotton shirt'),
  ('Warriors Navy Shirt', 'Navy shirt with Warriors text'),
  ('Cotton Hoodie', 'Cozy cotton hoodie');

3) (Optional but recommended) Add a full-text index for speed

Prompt

“Add a text search index so queries remain fast as the table grows.”

Run this:

create index if not exists products_name_fts
on public.products
using gin (to_tsvector('english', name));

(If you plan to search name + description, see the “Multi-column search” note at the end.)

4) Create a local project and install Supabase JS

Terminal instructions

mkdir supabase-textsearch-demo
cd supabase-textsearch-demo
npm init -y
npm i @supabase/supabase-js

5) Add environment variables

Create a .env file:

touch .env

Paste this (replace with your values):

SUPABASE_URL="YOUR_PROJECT_URL"
SUPABASE_ANON_KEY="YOUR_ANON_PUBLIC_KEY"

6) Create a script to run searches

Create a file:

touch search.mjs

Paste this code into search.mjs:

import 'dotenv/config'
import { createClient } from '@supabase/supabase-js'

const supabase = createClient(process.env.SUPABASE_URL, process.env.SUPABASE_ANON_KEY)

/**
 * Run a full-text search against the "products" table.
 * We’ll use the same idea shown in the video:
 * - choose a column (name)
 * - pass a search query
 * - optionally set type + config
 */
async function runSearch(query, { type = 'websearch', config = 'english' } = {}) {
  const { data, error } = await supabase
    .from('products')
    .select('id, name, description')
    .textSearch('name', query, { type, config })

  if (error) {
    console.error('Search error:', error)
    process.exit(1)
  }

  console.log(`\nQuery: ${query}`)
  console.table(data)
}

// --- Demo searches (mirrors the transcript examples) ---
await runSearch('shirt')                       // basic keyword
await runSearch('navy shirt')                  // AND behavior
await runSearch('navy OR cotton')              // OR behavior
await runSearch('"navy shirt"')                // phrase match
await runSearch('cotton -red')                 // exclude term

Install dotenv (needed for import 'dotenv/config')

npm i dotenv

7) Run the script

Terminal instructions

node search.mjs

You should see results printed for each query.

8) How to write “Google-like” search queries (websearch mode)

These patterns are directly reflected in the video transcript.

A) Single keyword

  • shirt

B) Multiple keywords = AND

  • navy shirt
    Returns rows that contain both terms.

C) OR queries

  • navy OR cotton
    Returns rows that contain either term.

D) Phrase queries

  • "navy shirt"
    Returns rows that contain the phrase in that exact sequence.

E) Exclusions

  • cotton -red
    Returns rows that contain cotton but do not contain red.

9) (Optional) Quick CLI prompt for trying your own query

If you want to type queries in the terminal instead of hardcoding them:

  1. Install a prompt helper:

npm i prompts

  1. Replace the bottom of your search.mjs with:

import prompts from 'prompts'

while (true) {
  const { q } = await prompts({
    type: 'text',
    name: 'q',
    message: 'Search products.name (type "exit" to quit):'
  })

  if (!q || q.toLowerCase() === 'exit') break

  await runSearch(q)
}

Run again:

node search.mjs

Notes & common upgrades

Multi-column search (name + description)

Postgres full-text search works best when you combine columns into one tsvector.

In SQL Editor, create an index like:

create index if not exists products_name_desc_fts
on public.products
using gin (
  to_tsvector('english', coalesce(name,'') || ' ' || coalesce(description,''))
);

Then in your app, you typically search against a computed tsvector column or a view/RPC (depending on how you want to structure it). If you tell me your exact schema, I can give you the cleanest version.

Done — this is the step-by-step companion guide for the video’s textSearch() workflow and query examples.

When This Approach Is the Right Choice

This pattern is ideal when:

  • Your dataset is in the thousands to millions of rows

  • Search is mostly keyword- or phrase-based

  • You want predictable, explainable results

  • You prefer minimal infrastructure

It’s often the fastest path from idea → usable product.

When You Might Outgrow It

There are cases where this isn’t enough:

  • Heavy typo tolerance

  • Semantic similarity (“things like this”)

  • AI-driven relevance ranking

The key insight: full-text search is not replaced by AI search—it complements it. Many successful systems start with database-native search and later layer embeddings or vector search only where needed.

The Bigger Lesson

Full-text search doesn’t have to be complicated.

By leaning on PostgreSQL’s mature search features through Supabase, you can:

  • Ship faster

  • Reduce costs

  • Avoid unnecessary architectural complexity

  • Still deliver powerful, flexible search

In many cases, the simplest solution is already sitting inside your database—you just need to use it.

Vibe Coding, SupabaseFrancesca Tabor